{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# from sklearn.ensemble import ExtraTreesRegressor\n",
    "# from sklearn.externals import joblib\n",
    "# from keras import Sequential\n",
    "# from keras.layers import LSTM, Dropout, Dense\n",
    "# from sklearn.linear_model import LinearRegression, SGDRegressor\n",
    "# from sklearn.metrics import mean_squared_error\n",
    "# from sklearn import preprocessing \n",
    "# from xgboost import XGBRegressor, plot_importance\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_por = pd.read_csv('student-por.csv')\n",
    "train_mat  = pd.read_csv('student-mat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
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       "      <th>famrel</th>\n",
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       "      <th>goout</th>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
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       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        4   0  11  11  \n",
       "1      5        3      3     1     1      3        2   9  11  11  \n",
       "2      4        3      2     2     3      3        6  12  13  12  \n",
       "3      3        2      2     1     1      5        0  14  14  14  \n",
       "4      4        3      2     1     2      5        0  11  13  13  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_por.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>5</td>\n",
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       "      <td>10</td>\n",
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      ],
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       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_mat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(649, 33)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_por.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(649, 33)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_por.dropna().shape #no null values in train_por"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 33)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 33)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_mat.dropna().shape #no null values in train_mat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_por['subject']='Portuguese'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_mat['subject']='Maths'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.concat([train_por, train_mat], axis=0) #combining two data files with subject as discriminant column to distinguish columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9</td>\n",
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       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>Portuguese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>Portuguese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>Portuguese</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other   \n",
       "\n",
       "      ...     freetime goout  Dalc  Walc  health absences  G1  G2  G3  \\\n",
       "0     ...            3     4     1     1       3        4   0  11  11   \n",
       "1     ...            3     3     1     1       3        2   9  11  11   \n",
       "2     ...            3     2     2     3       3        6  12  13  12   \n",
       "3     ...            2     2     1     1       5        0  14  14  14   \n",
       "4     ...            3     2     1     2       5        0  11  13  13   \n",
       "\n",
       "      subject  \n",
       "0  Portuguese  \n",
       "1  Portuguese  \n",
       "2  Portuguese  \n",
       "3  Portuguese  \n",
       "4  Portuguese  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('student.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train,pd.get_dummies(train['school'], prefix='school',dummy_na=True)],axis=1).drop(['school'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['sex'], prefix='sex',dummy_na=True)],axis=1).drop(['sex'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['address'], prefix='address',dummy_na=True)],axis=1).drop(['address'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['famsize'], prefix='famsize',dummy_na=True)],axis=1).drop(['famsize'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['Pstatus'], prefix='Pstatus',dummy_na=True)],axis=1).drop(['Pstatus'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['Mjob'], prefix='Mjob',dummy_na=True)],axis=1).drop(['Mjob'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['Fjob'], prefix='Fjob',dummy_na=True)],axis=1).drop(['Fjob'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['reason'], prefix='reason',dummy_na=True)],axis=1).drop(['reason'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['guardian'], prefix='guardian',dummy_na=True)],axis=1).drop(['guardian'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['schoolsup'], prefix='schoolsup',dummy_na=True)],axis=1).drop(['schoolsup'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['famsup'], prefix='famsup',dummy_na=True)],axis=1).drop(['famsup'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['paid'], prefix='paid',dummy_na=True)],axis=1).drop(['paid'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['activities'], prefix='activities',dummy_na=True)],axis=1).drop(['activities'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['nursery'], prefix='nursery',dummy_na=True)],axis=1).drop(['nursery'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['higher'], prefix='higher',dummy_na=True)],axis=1).drop(['higher'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['internet'], prefix='internet',dummy_na=True)],axis=1).drop(['internet'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['romantic'], prefix='romantic',dummy_na=True)],axis=1).drop(['romantic'],axis=1)\n",
    "train = pd.concat([train,pd.get_dummies(train['subject'], prefix='subject',dummy_na=True)],axis=1).drop(['subject'],axis=1);\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>...</th>\n",
       "      <th>higher_nan</th>\n",
       "      <th>internet_no</th>\n",
       "      <th>internet_yes</th>\n",
       "      <th>internet_nan</th>\n",
       "      <th>romantic_no</th>\n",
       "      <th>romantic_yes</th>\n",
       "      <th>romantic_nan</th>\n",
       "      <th>subject_Maths</th>\n",
       "      <th>subject_Portuguese</th>\n",
       "      <th>subject_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  Medu  Fedu  traveltime  studytime  failures  famrel  freetime  goout  \\\n",
       "0   18     4     4           2          2         0       4         3      4   \n",
       "1   17     1     1           1          2         0       5         3      3   \n",
       "2   15     1     1           1          2         0       4         3      2   \n",
       "3   15     4     2           1          3         0       3         2      2   \n",
       "4   16     3     3           1          2         0       4         3      2   \n",
       "\n",
       "   Dalc     ...       higher_nan  internet_no  internet_yes  internet_nan  \\\n",
       "0     1     ...                0            1             0             0   \n",
       "1     1     ...                0            0             1             0   \n",
       "2     2     ...                0            0             1             0   \n",
       "3     1     ...                0            0             1             0   \n",
       "4     1     ...                0            1             0             0   \n",
       "\n",
       "   romantic_no  romantic_yes  romantic_nan  subject_Maths  subject_Portuguese  \\\n",
       "0            1             0             0              0                   1   \n",
       "1            1             0             0              0                   1   \n",
       "2            1             0             0              0                   1   \n",
       "3            0             1             0              0                   1   \n",
       "4            1             0             0              0                   1   \n",
       "\n",
       "   subject_nan  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train = train.drop(['sex_nan'],axis=1)\n",
    "train = train.drop(['address_nan'],axis=1)\n",
    "train = train.drop(['famsize_nan'],axis=1)\n",
    "train = train.drop(['Pstatus_nan'],axis=1)\n",
    "train = train.drop(['Mjob_nan'],axis=1)\n",
    "train = train.drop(['Fjob_nan'],axis=1)\n",
    "train = train.drop(['reason_nan'],axis=1)\n",
    "train = train.drop(['guardian_nan'],axis=1)\n",
    "train = train.drop(['schoolsup_nan'],axis=1)\n",
    "train = train.drop(['famsup_nan'],axis=1)\n",
    "train = train.drop(['paid_nan'],axis=1)\n",
    "train = train.drop(['activities_nan'],axis=1)\n",
    "train = train.drop(['nursery_nan'],axis=1)\n",
    "train = train.drop(['higher_nan'],axis=1)\n",
    "train = train.drop(['internet_nan'],axis=1)\n",
    "train = train.drop(['romantic_nan'],axis=1)\n",
    "train = train.drop(['subject_nan'],axis=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.head()\n",
    "train.to_csv('students.csv', index=False)\n",
    "data = pd.read_csv('students.csv')\n",
    "data.head()\n",
    "data['FinalGrade'] = 'na'\n",
    "data.loc[(data.G3 >= 18) & (data.G3 <= 20), 'FinalGrade'] = 'Excellent'\n",
    "data.loc[(data.G3 >= 15) & (data.G3 <= 17), 'FinalGrade'] = 'Good' \n",
    "data.loc[(data.G3 >= 11) & (data.G3 <= 14), 'FinalGrade'] = 'Satisfactory' \n",
    "data.loc[(data.G3 >= 6) & (data.G3 <= 10), 'FinalGrade'] = 'Poor' \n",
    "data.loc[(data.G3 >= 0) & (data.G3 <= 5), 'FinalGrade'] = 'Failure' \n",
    "\n",
    "data.loc[(data.G1 >= 18) & (data.G1 <= 20), 'Grade1'] = 'Excellent'\n",
    "data.loc[(data.G1 >= 15) & (data.G1 <= 17), 'Grade1'] = 'Good' \n",
    "data.loc[(data.G1 >= 11) & (data.G1 <= 14), 'Grade1'] = 'Satisfactory' \n",
    "data.loc[(data.G1 >= 6) & (data.G1 <= 10), 'Grade1'] = 'Poor' \n",
    "data.loc[(data.G1 >= 0) & (data.G1 <= 5), 'Grade1'] = 'Failure' \n",
    "\n",
    "\n",
    "\n",
    "data.loc[(data.G2 >= 18) & (data.G2 <= 20), 'Grade2'] = 'Excellent'\n",
    "data.loc[(data.G2 >= 15) & (data.G2 <= 17), 'Grade2'] = 'Good' \n",
    "data.loc[(data.G2 >= 11) & (data.G2 <= 14), 'Grade2'] = 'Satisfactory' \n",
    "data.loc[(data.G2 >= 6) & (data.G2 <= 10), 'Grade2'] = 'Poor' \n",
    "data.loc[(data.G2 >= 0) & (data.G2 <= 5), 'Grade2'] = 'Failure' \n",
    "\n",
    "data.head(5)\n",
    "train=data\n",
    "lb=LabelEncoder()\n",
    "lb.fit(train['FinalGrade'])\n",
    "train['FinalGrade']=lb.transform(train['FinalGrade'])\n",
    "lb.fit(train['Grade2'])\n",
    "train['Grade2']=lb.transform(train['Grade2'])\n",
    "lb.fit(train['Grade1'])\n",
    "train['Grade1']=lb.transform(train['Grade1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=train[['FinalGrade']]\n",
    "train = train.drop(['G3'],axis=1);\n",
    "train = train.drop(['FinalGrade'],axis=1);\n",
    "train = train.drop(['G2'],axis=1);\n",
    "train = train.drop(['G1'],axis=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_val,y_train,y_val=train_test_split(train,y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rohith/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/rohith/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/rohith/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:459: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression,LinearRegression\n",
    "from sklearn.metrics import mean_squared_error,log_loss,roc_auc_score,accuracy_score,confusion_matrix\n",
    "lr=LogisticRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "y_pred=lr.predict(x_val)\n",
    "y_predict=lr.predict_proba(x_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred=y_pred.astype('int')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.800102388931476"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(y_val,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy_score(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr.score(x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rohith/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/rohith/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "svc = SVC()\n",
    "\n",
    "svc.fit(x_train, y_train)\n",
    "\n",
    "y_pred = svc.predict(x_val)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7279693486590039"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_val,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rohith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100,min_samples_leaf=2)\n",
    "\n",
    "random_forest.fit(x_train, y_train)\n",
    "\n",
    "y_pred_prob = random_forest.predict_proba(x_val)\n",
    "y_pred= random_forest.predict(x_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7739463601532567"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_val,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#log_loss(y_val,y_pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = list(range(0,24))\n",
    "import seaborn as sns    \n",
    "cm=confusion_matrix(y_val,y_pred)\n",
    "a4_dims = (11.7, 8.27)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(labels); ax.yaxis.set_ticklabels(labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of sensitivity = 55.40519372226689\n",
      "percentage of precision = 86.9733893557423\n",
      "Accuracy percentage = 90.95785440613027\n"
     ]
    }
   ],
   "source": [
    "FP = cm.sum(axis=0) - np.diag(cm)  \n",
    "FN = cm.sum(axis=1) - np.diag(cm)\n",
    "TP = np.diag(cm)\n",
    "TN = cm.sum() - (FP + FN + TP)\n",
    "\n",
    "total = lambda x : x.sum()/5\n",
    "# Sensitivity, hit rate, recall, or true positive rate\n",
    "TPR = TP/(TP+FN)\n",
    "print('percentage of sensitivity = '+str(total(TPR)*100))\n",
    "\n",
    "# Specificity or true negative rate\n",
    "TNR = TN/(TN+FP) \n",
    "\n",
    "# Precision or positive predictive value\n",
    "PPV = TP/(TP+FP)\n",
    "print('percentage of precision = '+str(total(PPV)*100))\n",
    "# Negative predictive value\n",
    "NPV = TN/(TN+FN)\n",
    "\n",
    "# Fall out or false positive rate\n",
    "FPR = FP/(FP+TN)\n",
    "\n",
    "# False negative rate\n",
    "FNR = FN/(TP+FN)\n",
    "\n",
    "# False discovery rate\n",
    "FDR = FP/(TP+FP)\n",
    "\n",
    "# Overall accuracy\n",
    "ACC = (TP+TN)/(TP+FP+FN+TN)\n",
    "print('Accuracy percentage = '+str(total(ACC)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(261,)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(261, 1)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "\n",
    "train = pd.read_csv('features.csv')\n",
    "train.head()\n",
    "y=train[['FinalGrade']]\n",
    "train = train.drop(['G3'],axis=1);\n",
    "train = train.drop(['FinalGrade'],axis=1);\n",
    "train = train.drop(['G2'],axis=1);\n",
    "train = train.drop(['G1'],axis=1);\n",
    "# y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1044, 5)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = label_binarize(y, classes=['Failure','Poor','Satisfactory','Good','Excellent'])\n",
    "n_classes = y.shape[1]\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(261, 5)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train,y,random_state=0)\n",
    "\n",
    "classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True))\n",
    "y_score = classifier.fit(x_train, y_train).decision_function(x_val)\n",
    "y_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:,i], y_score[:,i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Hr6ZevYr06dPQL/eTXgirwRmfyc/kZje8F5y9e+Pp3n0qe/bEAyAi9O3bqNAlN7AanCkAdpN84fH993vp02cmBw6cYvToxcyZ09/fIV0Qq8EZYwCYPHk9nTpN4cCBU1x/fU0mTbrJ3yFdMEtwxhRxKSlp/O1vnzNs2EKSk9O4//42LF58KxUrlvJ3aBfMmqjGFGFpaUqPHtNYsmQnxYqF8J//9GDYsBb+DivfWIIzpggLCRG6dq3Nxo1/MHfuAK666lJ/h5SvLMEZUwQdPZpA+fLhAIwe3Y4hQ5pRqVLhb5JmZn1wxhQhqsqLL66kXr0J7Np1FHAuAwnG5AZWgzOmyDh1Kok77ljAxx9vRQRWrPiNWrXK+zssn7IEZ0wRsHv3MaKiZrJhwx9ERBRn2rS+3HRTPX+H5XOW4IwJct98s5u+fWcRF3eaOnUqsHBhNA0bVvZ3WAXCp31wItJNRH4WkR0i8mgWyy8TkeUisl5ENolId1/GY0xRc/DgKbp1m0pc3GluuKE2a9bcWWSSG/iwBiciocBEoAsQC6wVkYWqutVjtbHALFX9j4g0AhYBkb6KyZiipkqVi3jttS7s2HGEf/yjC2FhReu8oi+bqG2AHaq6C0BEZgC9AM8Ep0AZ93VZYL8P4wkoNtKG8ZW4uFP88sthOnS4DICRI1v7OSL/8WU6rw7s9ZiOded5ega4VURicWpv92W1IxEZLiLrRGRdXFycL2ItcEUludkoIAVr06YDtG79Nt27T2P79kP+DsfvfFmDy2psFc00PRCYoqr/FJH2wIci0kRV087aSHUSMAmgVatWmfdRqNlIGya/zJmzlSFD5nP6dDJt2lSnTJkS/g7J73xZg4sFPO/7qMG5TdBhwCwAVV0FlAQq+TAmY4JOWpryzDMr6NfvY06fTua225ry9ddDqVYtwt+h+Z0vE9xaoK6I1BSR4kA0sDDTOnuAzgAi0hAnwQVHG9SYAnDyZBL9+s3i2We/JiREeO21Lrz/fhQlS9oVYODDJqqqpojIvcBiIBSYrKpbROQ5YJ2qLgQeBN4WkdE4zdehqhpUTVBjfGnbtjg++yyGsmVLMHNmP7p2rePvkAKKT9O8qi7COXngOe8pj9dbgQ6+jMGYYNa6dXWmTu1D06YXU69eRX+HE3CsHmtMIaKqTJy4lho1yhAV1QCAfv0a+TmqwGUJzphCIikplVGjPuOdd9ZTunRxdu68nypVLvJ3WAHNEpwxhcCBAyfp23cW3323l5Ilw3jrrR6W3LxgCc6YAPe///1OVNQM9u49TvXqEcyfH02rVtX8HVahYAnOmAA2f/52Bg2aQ0JCCu3b12DOnP5UrWrXt3mraN15a0whU6NGGVThjjuas3z57Zbc8shqcMYEmKSkVIoXDwWgVatqbNhwN/XqVSyUT5b3N68SnHsnwmWqusPH8RRaNjqIyQ8xMYeJiprJU091ZMCAJgDUr293L56vXJuoItID2Awsdaebi8g8XwdW2JxPcrORNoynJUt20qbNO2zdGsdrr60iLc1u6rlQ3tTgngPaAssBVHWDiNj9INmw0UFMXqkqb7yxmoceWkpamtKzZ30+/LA3ISHWJL1Q3iS4ZFU9lqn9b39ajMkHiYkpjBjxKe+/vxGAsWOv4dlnr7Pklk+8SXDbRKQ/ECIiNYG/Aat9G5YxRcPQofOZOXML4eFhTJkSRf/+jf0dUlDx5jKRe4GWQBowF0jESXLGmAv02GNX07BhJb777q+W3HzAmxpcV1V9BHgkfYaI9MFJdsaYPPrxx/20bOncidCs2SVs3jyS0FC7JNUXvHlXx2Yx74n8DsSYYJeamsbDDy+hVau3+eCDjRnzLbn5TrY1OBHpCnQDqovI6x6LyuA0V40xXjp2LJGBA+fwxRc7CAsLISEh2d8hFQk5NVEPAj/h9Llt8Zh/AjjnIc7GmKz9/PMhevacwS+/HKZixXBmz+5Pp06R/g6rSMg2wanqemC9iExV1cQCjMmYoLFoUQwDB87h+PEzXHFFFRYsiKZmzfL+DqvI8OYkQ3UReRFohPNQGABUtZ7PojImCCQnpzJmzGKOHz9Dnz4Nef/9KEqXLu7vsIoUbxLcFOAF4DXgRuAOrA/OmFwVKxbKvHkDmDdvO48+erVdvOsH3py+KaWqiwFUdaeqjgWu821YxhRO+/Yd5/XXV2VMN2xYmccfv8aSm594U4M7I859WjtFZASwD6ji27D8y0YGMedj9epYeveeyR9/nKRSpVIMGdLM3yEVed4kuNFAaeB+4EWgLPBXXwblb+eb3Gx0kKJrypQN3H33pyQlpXLddZF0727fhUCQa4JT1R/clyeA2wBEpIYvgwoUNjKIyU1KinPx7htvOD+Te+9tzeuvd6VYsVA/R2YglwQnIq2B6sC3qnpIRBrj3LJ1PVAkkpwx2Tl6NIEBA2azdOkuihULYeLE7tx1V0t/h2U8ZHuSQUReAqYCg4EvROQJnDHhNgJ2iYgp8kJDQ4iNPU6VKhfx1Ve3W3ILQDnV4HoBzVQ1QUQqAPvd6Z8LJjRjApOqIiKUKVOCTz4ZSLFioVx2WVl/h2WykNNlIomqmgCgqkeA7ZbcTFGmqvzf/33D8OGfoOqM+Vq7dgVLbgEspxpcLRFJHxJJgEiPaVS1j08jMyaAnD6dzF//uoCZM7cgAnff3coevlwI5JTg+maanuDLQIwJVHv2xBMVNYP16/8gIqI4U6f2seRWSOR0s/2XBRmIMYHo22/30KfPTOLiTlOnTgUWLIimUaPK/g7LeMke/GxMNpYu3UmPHtNITk6jS5dazJjRjwoVwv0dlskDnw4lKiLdRORnEdkhIlmOISci/UVkq4hsEZFpvozHmLxo3/5SGjaszOjR7Vi0aLAlt0LI6xqciJRQ1TN5WD8UmAh0AWKBtSKyUFW3eqxTF3gM6KCqR0UkqO9xNYHv8OHTlCpVjPDwYpQuXZzvv/8rF11kQxwVVt482b6NiGwGYtzpZiLyLy/23QbYoaq7VDUJmIFzbZ2nu4CJqnoUQFUP5il6Y/LR5s0HaNXqbe6668/LQCy5FW7e1ODGAzcB8wFUdaOIeDNcUnVgr8d0LNA20zr1AETkOyAUeEZVv/Bi3z5ho4gUXXPnbmPIkHmcOpVMpUqlOHEiiTJlSvg7LHOBvOmDC1HV3ZnmpXqxXVYDYGmm6TCgLtAJGAi8IyLlztmRyHARWSci6+Li4rw49PnxTG42MkjRkJamPPvsCvr2ncWpU8kMHnwFK1cOteQWJLypwe0VkTaAuv1q9wG/eLFdLHCpx3QNnNu9Mq+zWlWTgV9F5GechLfWcyVVnQRMAmjVqlXmJJnvbBSRouHkySRuv30+c+duQwT+8Y+/8NBDV+EMf2iCgTc1uJHAGOAy4ADQzp2Xm7VAXRGpKSLFgWhgYaZ15uOODiwilXCarLu8C92YC/PSS98wd+42ypQpwaefDuLhhztYcgsy3tTgUlQ1Oq87VtUUEbkXWIzTvzZZVbeIyHPAOlVd6C67QUS24jR7H1bVw3k9ljHnY+zYjuzadYynn76WBg0q+Tsc4wPeJLi1btNxJjBXVU94u3NVXQQsyjTvKY/XilM7HOPtPo05X6rK9Ok/0atXfS66qDjh4cWYPj3zHYkmmOTaRFXV2jhP1WoJbBaR+SKS5xqdMf6UlJTKiBGfMnjwXIYOXZBxGYgJbl7dyaCq36vq/UAL4DjOQJjGFAoHD56ic+cPmDTpf5QoEUqvXvWtr62IyLWJKiKlcS7QjQYaAguAq3wclzH5Yv3634mKmsmePfFUqxbB/PkDaN26ur/DMgXEmz64n4BPgFdU9Rsfx2NMvpk1awtDh84nISGFdu1qMHduf6pWjfB3WKYAeZPgaqmqPcneFDqLF+8gISGFoUOb85//9KBkSRs8p6jJ9hMXkX+q6oPAHBE5p0fWRvQ1ge7f/+7B9dfXZNCgK6zPrYjK6U/aTPd/G8nXFAo7dx7hkUeWMXlyL8qUKUGJEmEMHtzU32EZP8ppRN817suGqnpWknMv4C0UI/7aDfRFw7Jlu+jf/2OOHk2kevUI3nzzRn+HZAKAN5eJ/DWLecPyOxBfyWtys5vsCxdV5c03V9O160ccPZrIzTfX4/nnr/d3WCZA5NQHNwDn0pCank/TAiKAY74OLL/ZDfTB58yZFEaO/Iz33tsAwOOPX83zz19PSIj1txlHTn1wa4DDOKOATPSYfwJY78ugjMlNQkIynTt/wKpVsYSHh/Hee70YMKCJv8MyASanPrhfgV+BZQUXjjHeCQ8vRvPmlxAbe5z586Np0aKqv0MyASjbPjgR+dr9/6iIHPH4d1REjhRciMb86cSJPx8L8uab3fjxx+GW3Ey2cjrJkD4seSWgsse/9GljCkxqahqPPLKUli0ncfRoAgDFioVSufJFfo7MBLJsE5zH3QuXAqGqmgq0B+4G7FtlCkx8fCI9e87glVe+Z9euo3zzzR5/h2QKCW8uE5mPM1x5beADnBvu7fmlpkD88sth2rZ9h0WLYqhYMZylS2+jZ8/6/g7LFBLe3JyXpqrJItIHeENVx4uInUU1PvfFFzuIjp5NfPwZrriiCgsWRFOzZnl/h2UKEa+GLBeRW4DbgCh3XjHfhWQMbNsWR48e00hLU3r3bsAHH/SmdGl7RqnJG28S3F+Be3CGS9olIjWB6b4NyxR1DRtW5oEH2hIRUYKnnrrWLt415yXXBKeqP4nI/UAdEWmA87T6F30fmilq9u8/wbFjiTRq5Jykf+21G2wUEHNBvBnR9xrgQ2AfzsOcLxGR21T1O18HZ4qOH36IpXfvmZQoEcbatXdRqVIpS27mgnnTRB0HdFfVrQAi0hAn4bXyZWDestFCCr/339/A8OGfkpSUSqdOkf4OxwQRby4TKZ6e3ABUdRsQML293iQ3GyEkMKWkpPHgg4sZOnQBSUmp3HNPK5YsuZVKlUr5OzQTJLypwf1PRP6LU2sDGEwA3mxvo4UULkePJhAdPYclS3YSFhbCxIndGT68pb/DMkHGmwQ3Argf+DtOH9xK4F++DMoEv6+/3s2SJTupXLkUc+b055prLvd3SCYI5ZjgROQKoDYwT1VfKZiQTFEQFdWAf/+7O9271+Xyy8v5OxwTpHIaTeRxnNu0BgNLRSSrkX2N8Yqq8o9/fMu6dfsz5o0c2dqSm/GpnGpwg4GmqnpKRCoDi4DJBROWCSanTyczbNhCZsz4iUsvXcvPP99LeLjdDGN8L6cEd0ZVTwGoapyIeHPG1Ziz7N0bT1TUTP73v98pXbo4EyZ0t+RmCkxOCa6Wx7MYBKjt+WwGey6qyc133+2hT59ZHDx4itq1y7NgQTSNG1fxd1imCMkpwfXNNG3PRzVee//9Ddx11yckJ6fRuXNNZs26hQoVwv0dlilicnomQ6F47qkJTGXKlCA5OY3772/DP//ZlbAw6+EwBc+b6+CM8UpqahqhoU4i6927IevX303z5pf4OSpTlPn0z6qIdBORn0Vkh4g8msN6/URERSQg7m81ebd58wGaNPkPq1btzZhnyc34m9cJTkRK5GXHIhKK8zzVG4FGwEARaZTFehE4d0r8kJf9m8Axf/522rd/l+3bD/Hii9/4OxxjMngzXFIb4F2gLHCZiDQD7lTV+3LZtA3O2HG73P3MAHoBWzOt9zzwCvCQNwEfOXKEZ5991ptVjY+lpSkvvLCSp59eAcCgQVfwzjs3+zcoYzx4U4MbD9yE85R7VHUjfz5SMCfVgb0e07HuvAwiciVwqap+mtOORGS4iKwTkXWJiYnnLLfRQgreqVNJ9O//MU8/vQIR+Mc//sJHH/W2a9xMQPHmJEOIqu7ONPhgqhfbZTVaoWYsdC4cHgcMzW1HqjoJmARQrVo1BRs9xJ9UlR49pvH117spU6YE06b1oUePev4Oy5hzeJPg9rrNVHX71e4DfvFiu1icZ6qmqwHs95iOAJoAK9zkeQmwUER6quo6b4I3/iEi/P3vHThw4BTz5g2gQYNK/g7JmCx500QdCYwBLgMOAO3ceblZC9QVkZoiUhyIBhamL1TVeFWtpKqRqhoJrAYsuQWwrVvjMl53716XTZtGWHIzAS3XBKeqB1U12k1GldzXh7zYLgXL/WpFAAAag0lEQVS4F1gMbANmqeoWEXlORHpeeOimoCQlpTJixKc0a/YWX3/9W8b8YsVC/ReUMV7w5izq23j0naVT1eG5bauqi3BGIfGc91Q263bKbX+m4MXFnaJv31l8880eSpQI5Y8/Tvo7JGO85k0f3DKP1yWB3px9dtQEqQ0b/qBXrxns2RNP1aqlmT8/mjZtque+oTEBwpvnos70nBaRD4GlPovIBISPP97C0KELOH06mTZtqjNv3gCqVYvwd1jG5Mn53KpVE7AB9INYfHwiI0d+xunTyQwZ0oyvvx5qyc0USt70wR3lzz64EOAIkO19pabwK1u2JDNn9mPjxgOMHt3OHsBsCq3cHjojQDOcp9oDpKnqOSccTOG3c+cRvv9+L7fd1gyAzp1r0blzLT9HZcyFyTHBqaqKyDxVtQdWBrEvv9xF//6zOXYskUsvLWtPlzdBw5s+uDUi0sLnkZgCp6qMH/8DXbt+xJEjCdx4Yx1atKjq77CMyTfZ1uBEJMy9WPdq4C4R2QmcwrnHVFXVkl4hduZMCqNGLeLdd9cD8NhjV/P889dlDFhpTDDIqYm6BmgBRBVQLKaAHDhwkj59ZvH993spWTKMyZN7MnDgFf4Oy5h8l1OCEwBV3VlAsZgCkpiYQkzMYWrUKMP8+QNo2bKav0MyxidySnCVRWRMdgtV9XUfxGMKwOWXl2PRosFcemkZLr64tL/DMcZnckpwoUBpsh7XzRQiqalpjB37FWXLluTRR68GoFUrq7WZ4JdTgvtdVZ8rsEiMT8THJzJ48Fw++yyGYsVCGDToCi67rKy/wzKmQOTaB2cKr5iYw/TsOYPt2w9RoUI4H398iyU3U6TklOA6F1gUJt8tWbKTAQOci3cbN67MwoUDqVWrvL/DMqZA5fRk+yMFGYjJP9Onb+bWW+eRlqZERTXggw+iiIjI01MfjQkK9mT7INSx4+Vccklp7rzzSp5+uhMhIdbbYIomS3BB4tCh01SoEE5IiFC9ehm2br2HsmVL+jssY/zK7ssJAmvW7KNZs7d49tkVGfMsuRljCa7Q+/DDjXTs+B7795/g6693k5zszSNrjSkaLMEVUqmpaTz88BKGDJnPmTOpjBzZiqVLb7MnXRnjwfrgCqGjRxMYOHAOixfvJCwshH/960ZGjGjl77CMCTiW4AqhBx5YzOLFO6lUqRSzZ9/CtddG+jskYwKSJbhC6JVX/sLhw6eZMKE7kZHl/B2OMQHL+uAKAVVl5syfSE1NA+Dii0vz6aeDLLkZkwtLcAEuISGZW2+dR3T0HJ544it/h2NMoWJN1AAWG3ucqKgZ/Pjj75QuXZx27Wr4OyRjChVLcAHq++/30qfPTA4cOEXNmuVYuHAgTZpU8XdYxhQq1kQNQJMnr+e6697nwIFTXH99TdauvcuSmzHnwRJcgElLU6ZO3UxSUir33deGL74YTMWKpfwdljGFkjVRA0xIiDBrVj+++GIHgwc39Xc4xhRqVoMLAFu2HOTOOxeSkuJcBlKxYilLbsbkA58mOBHpJiI/i8gOEXk0i+VjRGSriGwSkS9F5HJfxhOIFizYTrt27/Luu+sZN26Vv8MxJqj4LMGJSCgwEbgRaAQMFJFGmVZbD7RS1abAbOAVX8UTaFSVF15YSVTUTE6eTCI6ugmjRrXxd1jGBBVf1uDaADtUdZeqJgEzgF6eK6jqclU97U6uBorEhV6nTiUxYMBsnnxyOSLw0kudmTatD6VKFfN3aMYEFV+eZKgO7PWYjgXa5rD+MODzrBaIyHBgOEDVqlWpW7dufsVY4A4dOk2XLh+yYcMfREQUZ9q0vtx0Uz1/h2VMUPJlgsvqQQCa5YoitwKtgGuzWq6qk4BJANWqVdNBgwblV4wFrnz5klxySWnq1KnAwoXRNGxY2d8hGRO0fJngYoFLPaZrAPszryQifwGeAK5V1TM+jMevEhKSCQ8vRmhoCNOn90VVKV8+3N9hGRPUfNkHtxaoKyI1RaQ4EA0s9FxBRK4E/gv0VNWDPozFb5KTU7nnns/o2vUjkpKc4cTLlStpyc2YAuCzGpyqpojIvcBiIBSYrKpbROQ5YJ2qLgReBUoDH4sIwB5V7emrmAraoUOnueWWj1mx4jeKFw9l3br9XHXVpblvaIzJF6KaZbdYwKpWrZru339OSzfgbNp0gF69ZvDbb8eoWrU08+YNoG3bInGS2Jh8JyI/qmqex+W3W7V8YO7cbQwZMo9Tp5Jp3boa8+dHU61ahL/DMqbIsQSXz5Yv/5W+fWcBcNttTZk06WZKlrS32Rh/sF9ePrv22kj69GnIVVfVYMyY9rh9i8YYP7AElw9+/fUoJUqEUa1aBCEhwuzZt1hiMyYA2GgiF2j58l9p3fpt+vSZSWJiCoAlN2MChCW486SqTJy4hi5dPuTw4QQqViyVcZ2bMSYwWII7D0lJqdx996fce+/npKYqjzzSgYULoylTpoS/QzPGeLA+uDw6ePAUffvO4ttv91CyZBjvvtuTQYOu8HdYxpgsWILLo+nTN/Ptt3uoXj2C+fOjadWqmr9DMsZkwxJcHt1/f1vi488wfHhLLrmktL/DMcbkwPrgcpGWprz44kr27IkHnDOkTz11rSU3YwoBS3A5OH78DFFRMxg7djn9+s0iLa1w3bdrTFFnTdRs7NhxhF69ZrB1axzly5fkpZc6ExJi17cZU5hYgsvC0qU7GTBgNkePJtKoUWUWLoymdu0K/g6rQCUnJxMbG0tiYqK/QzFFSMmSJalRowbFiuXP80kswWUyfvwPjB69mLQ0pWfP+nz4Ye8ieX1bbGwsERERREZG2p0ZpkCoKocPHyY2NpaaNWvmyz6tDy6TpKRU0tKUsWOvYd68AUUyuQEkJiZSsWJFS26mwIgIFStWzNdWg9XgcM6UpvevPfhgezp0uJT27W3kXUtupqDl93euyNfg1q7dx5VX/pddu44Czhtsyc2Y4FCkE9zUqZu45pr32LTpAC+//K2/wzGZhIaG0rx5c5o0acLNN9/MsWPHMpZt2bKF66+/nnr16lG3bl2ef/55PIff//zzz2nVqhUNGzakQYMGPPTQQ/4oQo7Wr1/PnXfe6e8wcvTSSy9Rp04d6tevz+LFi7Nc58svv6RFixY0b96cq6++mh07dmQsmzVrFo0aNaJx48akP+4zLi6Obt26FUj8qGqh+le1alW9UCkpqfrww0sUnlF4RocPX6hnzqRc8H6DydatW/0dgl500UUZr4cMGaIvvPCCqqqePn1aa9WqpYsXL1ZV1VOnTmm3bt10woQJqqq6efNmrVWrlm7btk1VVZOTk3XixIn5GltycvIF76Nfv366YcOGAj1mXmzZskWbNm2qiYmJumvXLq1Vq5ampJz7O6lbt27G92XixIl6++23q6rqL7/8os2bN9cjR46oquqBAwcythk6dKh+++23WR43q+8ezoOq8pwvilwf3LFjiQwaNIfPP99BWFgIb77ZjZEjW1l/Uw6effZZn+z36aef9nrd9u3bs2nTJgCmTZtGhw4duOGGGwAoVaoUEyZMoFOnTowaNYpXXnmFJ554ggYNGgAQFhbGPffcc84+T548yX333ce6desQEZ5++mn69u1L6dKlOXnyJACzZ8/m008/ZcqUKQwdOpQKFSqwfv16mjdvzrx589iwYQPlypUDoE6dOnz33XeEhIQwYsQI9uzZA8Abb7xBhw4dzjr2iRMn2LRpE82aNQNgzZo1PPDAAyQkJBAeHs57771H/fr1mTJlCp999hmJiYmcOnWKr776ildffZVZs2Zx5swZevfunfH5REVFsXfvXhITE/nb3/7G8OHDvX5/s7JgwQKio6MpUaIENWvWpE6dOqxZs4b27duftZ6IcPz4cQDi4+OpVs25P/vtt99m1KhRlC9fHoAqVapkbBMVFcXUqVPPeV/yW5FKcMnJqVx99WS2bImjYsVwZs/uT6dOkf4Oy+QiNTWVL7/8kmHDhgFO87Rly5ZnrVO7dm1OnjzJ8ePH+emnn3jwwQdz3e/zzz9P2bJl2bx5MwBHjx7NdZtffvmFZcuWERoaSlpaGvPmzeOOO+7ghx9+IDIykosvvphBgwYxevRorr76avbs2UPXrl3Ztm3bWftZt24dTZo0yZhu0KABK1euJCwsjGXLlvH4448zZ84cAFatWsWmTZuoUKECS5YsISYmhjVr1qCq9OzZk5UrV9KxY0cmT55MhQoVSEhIoHXr1vTt25eKFSueddzRo0ezfPnyc8oVHR3No48+eta8ffv20a5du4zpGjVqsG/fvnO2feedd+jevTvh4eGUKVOG1atXZ7xXAB06dCA1NZVnnnkmo2naqlUrxo4dm+v7faGKVIIrViyUESNaMWnSjyxYEE3NmuX9HVKhkJeaVn5KSEigefPm/Pbbb7Rs2ZIuXboATrdKdjXuvNTEly1bxowZMzKm02saObnlllsIDQ0FYMCAATz33HPccccdzJgxgwEDBmTsd+vWrRnbHD9+nBMnThAR8eeT1X7//XcqV66cMR0fH8/tt99OTEwMIkJycnLGsi5dulChgnOh+ZIlS1iyZAlXXnkl4NRCY2Ji6NixI+PHj2fevHkA7N27l5iYmHMS3Lhx47x7c+CsPs10Wb2/48aNY9GiRbRt25ZXX32VMWPG8M4775CSkkJMTAwrVqwgNjaWa665hp9++oly5cpRpUoVCuLxn0F/kkFVM86QAowa1Zo1a+6y5FYIhIeHs2HDBnbv3k1SUhITJ04EoHHjxqxbt+6sdXft2kXp0qWJiIigcePG/Pjjj7nuP7tE6Tkv8zVZF110Ucbr9u3bs2PHDuLi4pg/fz59+vQBIC0tjVWrVrFhwwY2bNjAvn37zkpu6WXz3PeTTz7Jddddx08//cQnn3xy1jLPY6oqjz32WMa+d+zYwbBhw1ixYgXLli1j1apVbNy4kSuvvDLL68lGjx5N8+bNz/n38ssvn7NujRo12Lt3b8Z0bGxsRvMzXVxcHBs3bqRt27aAk/S///77jO179epFsWLFqFmzJvXr1ycmJibjfQ0PDz/nmPktqBNcQkIyQ4bMp0WL//Lzz4cA58trj/ErXMqWLcv48eN57bXXSE5OZvDgwXz77bcsW7YMcGp6999/P3//+98BePjhh/m///u/jCZSWloar7/++jn7veGGG5gwYULGdHoT9eKLL2bbtm0ZTdDsiAi9e/dmzJgxNGzYMKO2lHm/GzZsOGfbhg0bnnW2MT4+nurVqwMwZcqUbI/ZtWtXJk+enNFHuG/fPg4ePEh8fDzly5enVKlSbN++PaOZmNm4ceMykqPnv8zNU4CePXsyY8YMzpw5w6+//kpMTAxt2rQ5a53y5csTHx+f8V4vXbqUhg0bAk4/W3pz+NChQ/zyyy/UqlULcJqvnk10XwnaBLdv33E6dpzCRx9tIiUljV9/PZb7RiZgXXnllTRr1owZM2YQHh7OggULeOGFF6hfvz5XXHEFrVu35t577wWgadOmvPHGGwwcOJCGDRvSpEkTfv/993P2OXbsWI4ePUqTJk1o1qxZxo/x5Zdf5qabbuL666+natWqOcY1YMAAPvroo4zmKcD48eNZt24dTZs2pVGjRrz11lvnbNegQQPi4+M5ceIEAH//+9957LHHMvqrsnPDDTcwaNAg2rdvzxVXXEG/fv04ceIE3bp1IyUlhaZNm/Lkk0+e1Xd2vho3bkz//v1p1KgR3bp1Y+LEiRnN8+7du7N//37CwsJ4++236du3L82aNePDDz/k1VdfBZxkXLFiRRo1asR1113Hq6++mvFHYPny5fTo0eOCY8yNZNXODmTVqlXT3Nruq1fH0rv3TP744ySRkeVYuDCaK664uIAiDA7btm3L+EtsfGPcuHFEREQE/LVwvtCxY0cWLFiQZb9nVt89EflRVVvl9ThBV4ObMmUD1147hT/+OEmnTpGsXXuXJTcTkEaOHEmJEkXvXue4uDjGjBnj1UmdCxVUCe63344xfPgnJCWlMmpUa5YsuZVKlUr5OyxjslSyZEluu+02f4dR4CpXrkxUVFSBHCuoetsjI8vx3//eREpKGnfd1TL3DUyOcrocwxhfyO8us0Kf4LZujWPPnni6dasDwB13XOnniIJDyZIlOXz4sA2ZZAqMuuPBlSxZMt/2WagT3Cef/MzgwXNRhTVr7qRhw8q5b2S8UqNGDWJjY4mLi/N3KKYISR/RN78UygSnqrz00reMHfsVqtC/f2Muv7ycv8MKKukXZxpTmPn0JIOIdBORn0Vkh4iccyWhiJQQkZnu8h9EJDK3farCwIFzeOKJrwB48cXrmTGjL6VK5c8Y7saY4OGzGpyIhAITgS5ALLBWRBaq6laP1YYBR1W1johEA/8ABpy7tz8dOgQzZ24hIqI4U6f24eab6/uqCMaYQs6XNbg2wA5V3aWqScAMoFemdXoB77uvZwOdJZce7ZQUqF27PKtX32nJzRiTI1/2wVUH9npMxwJts1tHVVNEJB6oCBzyXElEhgPpg1ud2bnzbz81bvw3nwRdwCqRqayFWLCUJVjKAcFVlvOqzfgywWVVE8t8kYs366Cqk4BJACKy7nxu2QhEVpbAEyzlgOAry/ls58smaizg+fSWGkDmm0gz1hGRMKAscMSHMRljihBfJri1QF0RqSkixYFoYGGmdRYCt7uv+wFfaWG7+98YE7B81kR1+9TuBRYDocBkVd0iIs/hPEBiIfAu8KGI7MCpuUV7setJvorZD6wsgSdYygFWlsI3XJIxxngrqEYTMcYYT5bgjDFBK2ATnC9u8/IHL8oxRkS2isgmEflSRC73R5zeyK0sHuv1ExEVkYC9RMGbsohIf/ez2SIi0wo6Rm958R27TESWi8h693vW3R9x5kZEJovIQRH5KZvlIiLj3XJuEpEWue70fJ4W7et/OCcldgK1gOLARqBRpnXuAd5yX0cDM/0d93mW4zqglPt6ZCCWw9uyuOtFACuB1UArf8d9AZ9LXWA9UN6druLvuC+gLJOAke7rRsBv/o47m7J0BFoAP2WzvDvwOc71s+2AH3LbZ6DW4Hxym5cf5FoOVV2uqqfdydU41wsGIm8+E4DngVeAc59ZFzi8KctdwERVPQqgqgcLOEZveVMWBcq4r8ty7vWoAUFVV5LzdbC9gA/UsRooJyI5PhUoUBNcVrd5Vc9uHVVNAdJv8wok3pTD0zCcv1CBKNeyiMiVwKWq+mlBBnYevPlc6gH1ROQ7EVktIt0KLLq88aYszwC3ikgssAi4r2BCy3d5/T0F7Hhw+Xabl595HaOI3Aq0Aq71aUTnL8eyiEgIMA4YWlABXQBvPpcwnGZqJ5xa9Tci0kRVA+35k96UZSAwRVX/KSLtca49baKqab4PL1/l+TcfqDW4YLnNy5tyICJ/AZ4AeqrqmQKKLa9yK0sE0ARYISK/4fSRLAzQEw3efr8WqGqyqv4K/IyT8AKNN2UZBswCUNVVQEmcG/ELG69+T2fxd8diNp2JYcAuoCZ/dpw2zrTOKM4+yTDL33GfZzmuxOkkruvveC+0LJnWX0HgnmTw5nPpBrzvvq6E0zSq6O/Yz7MsnwND3dcN3aQg/o49m/JEkv1Jhh6cfZJhTa7783eBcihod+AX98f/hDvvOZxaDjh/hT4GdgBrgFr+jvk8y7EMOABscP8t9HfM51uWTOsGbILz8nMR4HVgK7AZiPZ3zBdQlkbAd27y2wDc4O+YsynHdOB3IBmntjYMGAGM8PhMJrrl3OzN98tu1TLGBK1A7YMzxpgLZgnOGBO0LMEZY4KWJThjTNCyBGeMCVqW4IogEUkVkQ0e/yJzWDcyu9Ed8njMFe6IFxvd25/y/JQkERkhIkPc10NFpJrHsndEpFE+x7lWRJp7sc0DIlLqQo9t8p8luKIpQVWbe/z7rYCOO1hVm+EMkvBqXjdW1bdU9QN3cihQzWPZnXr2Q8UvRHqc/8a7OB8ALMEFIEtwBsioqX0jIv9z/12VxTqNRWSNW+vbJCJ13fm3esz/r4iE5nK4lUAdd9vO7jhlm93xwEq481/2GCfvNXfeMyLykIj0w7lvd6p7zHC35tVKREaKyCseMQ8VkX+dZ5yr8LiZW0T+IyLr3PHhnnXn3Y+TaJeLyHJ33g0issp9Hz8WkdK5HMf4iCW4oinco3k6z513EOiiqi2AAcD4LLYbAbypqs1xEkysiDR01+/gzk8FBudy/JuBzSJSEpgCDFDVK3BuOxopIhWA3ji3HDUFXvDcWFVnA+twalrNVTXBY/FsoI/H9ABg5nnG2Q2Y7zH9hDrPGW0KXCsiTVV1PM6tT9ep6nUiUgkYC/zFfS/XAWNyOY7xkUAdTcT4VoL7I/dUDJjg9jml4gwXlNkq4AkRqQHMVdUYEekMtATWusPxheMky6xMFZEE4DecIXvqA7+q6i/u8vdx7jGegDOe3Dsi8hng9fBLqhonIrtEpB0Q4x7jO3e/eYnzIpzBJD1Hje0vIsNxfjdVcW6B2pRp23bu/O/c4xTHed+MH1iCM+lG49wT2wynZn/OgJWqOk1EfsC56XmxiNyJc3/g+6r6mBfHGKyqGU8oF5Esx+9T55GTbYDOOAMp3Atcn4eyzAT6A9uBeaqq7mCoXseJc9/myzj3PvYRkZrAQ0BrVT0qIlNw7ofOTIClqjowD/EaH7EmqklXFvhdnTHCbsOpvZxFRGoBu9xm2UKcptqXQD8RqeKuU0G8f67EdiBSROq407cBX7t9VmVVdRFOB35WZzJP4AzRlJW5QBTOOGgz3Xl5ilNVk3Gamu3c5m0Z4BQQLyIXAzdmE8tqoEN6mUSklIhkVRs2BcASnEn3b+B2EVmN0zw9lcU6A4CfRGQD0ABn+OitOIlgiYhsApbiNN9ypaqJwB3AxyKyGUgD3sJJFp+6+/sap3aZ2RTgrfSTDJn2exRnFJDLVXWNOy/Pcbp9e/8EHlLVjTjPaNgCTMZp9qabBHwuIstVNQ7nDO909zircd4r4wc2mogxJmhZDc4YE7QswRljgpYlOGNM0LIEZ4wJWpbgjDFByxKcMSZoWYIzxgSt/weduQPiYAaD1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "lw = 2\n",
    "for i in (0,1):\n",
    "    plt.subplot(1,2,i+1)\n",
    "    plt.plot(fpr[i], tpr[i], color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[i])\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic label'+str(i))\n",
    "    plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.plot()\n",
    "\n",
    "plt.figure()\n",
    "lw = 2\n",
    "for i in (2,3):\n",
    "    plt.subplot(1,2,i-1)\n",
    "    plt.plot(fpr[i], tpr[i], color='red',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[i])\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic label'+str(i))\n",
    "    plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.plot()\n",
    "\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(fpr[4], tpr[4], color='grey',\n",
    "     lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[4])\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic label'+str(4))\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area Under the Curve with label 0 is 0.7366515837104073\n",
      "Area Under the Curve with label 1 is 0.8226164079822617\n",
      "Area Under the Curve with label 2 is 0.9732026143790851\n",
      "Area Under the Curve with label 3 is 0.8939724037763254\n",
      "Area Under the Curve with label 4 is 0.8550135501355014\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute macro-average ROC curve and ROC area\n",
    "\n",
    "# First aggregate all false positive rates\n",
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "\n",
    "# Then interpolate all ROC curves at this points\n",
    "mean_tpr = np.zeros_like(all_fpr)\n",
    "for i in range(n_classes):\n",
    "    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "# Finally average it and compute AUC\n",
    "mean_tpr /= n_classes\n",
    "\n",
    "fpr[\"macro\"] = all_fpr\n",
    "tpr[\"macro\"] = mean_tpr\n",
    "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "\n",
    "# Plot all ROC curves\n",
    "plt.figure()\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "         label='micro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"micro\"]),\n",
    "         color='deeppink', linestyle=':', linewidth=4)\n",
    "\n",
    "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "         label='macro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"macro\"]),\n",
    "         color='navy', linestyle=':', linewidth=4)\n",
    "\n",
    "for i in range(n_classes):\n",
    "    plt.plot(fpr[i], tpr[i], lw=2,\n",
    "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
    "             ''.format(i, roc_auc[i]))\n",
    "    print('Area Under the Curve with label '+str(i)+' is '+str(roc_auc[i]))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.rcParams[\"figure.figsize\"] = (10,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f score = 0.5971119508616101\n",
      "percentage of recall score = 0.5540519372226689\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, recall_score, classification_report\n",
    "print('f score = ' + str(f1_score(y_val, y_pred, average=\"macro\")))\n",
    "print('percentage of recall score = '+str(recall_score(y_val, y_pred, average=\"macro\"))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Failure       1.00      0.17      0.29         6\n",
      "        Poor       1.00      0.20      0.33        15\n",
      "Satisfactory       0.83      0.71      0.76        41\n",
      "        Good       0.75      0.84      0.79        91\n",
      "   Excellent       0.78      0.86      0.82       108\n",
      "\n",
      "   micro avg       0.77      0.77      0.77       261\n",
      "   macro avg       0.87      0.55      0.60       261\n",
      "weighted avg       0.79      0.77      0.76       261\n",
      "\n"
     ]
    }
   ],
   "source": [
    "target_names = ['Failure','Poor','Satisfactory','Good','Excellent']\n",
    "print(classification_report(y_val, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6817999801108086"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import fbeta_score\n",
    "fbeta_score(y_val,y_pred,average='macro', beta=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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